Question

我对使用Linux有点陌生，我将使用自己的数据集构建对象检测模型。现在我正在准备它。我被困在将csv文件转换为tfrecord的步骤中。我已经按照网上搜索的所有必要步骤进行操作，但是当我要在终端中运行此命令时：

"python3 generate_tfrecord.py --csv_input=train.csv --output_path=data/train.record"

它显示此错误：

用法：generate_tfrecord.py [global_opts] cmd1 [cmd1_opts] [cmd2 [cmd2_opts] ...] 或：generate_tfrecord.py --help [cmd1 cmd2 ...] 或：generate_tfrecord.py --help-commands 或：generate_tfrecord.py cmd --help
error: option --csv_input not recognized

我正在使用Linux Ubuntu。我已经尝试过跑步

python setup.py build
python setup.py install

下面是代码：

from __future__ import division
from __future__ import print_function
from __future__ import absolute_import

import sys
import os
import io
import pandas as pd
import tensorflow as tf
from setuptools import find_packages
from setuptools import setup


setup(
    name='object_detection',
    version='0.1',
    install_requires=REQUIRED_PACKAGES,
    include_package_data=True,
    packages=[p for p in find_packages() if p.startswith('object_detection')],
    description='Tensorflow Object Detection Library',
)

from PIL import Image
from object_detection.utils import dataset_util
from collections import namedtuple, OrderedDict


flags = tf.app.flags
flags.DEFINE_string('csv_input', '', 'Path to the CSV input')
flags.DEFINE_string('output_path', '', 'Path to output TFRecord')
flags.DEFINE_string('image_dir', '', 'Path to images')
FLAGS = flags.FLAGS


# TO-DO replace this with label map
def class_text_to_int(row_label):
    if row_label == 'hex head self drilling screw':
        return 1
    if row_label == 'phillips flat head self tapping screw':
        return 2
    else:
        return None


def split(df, group):
    data = namedtuple('data', ['filename', 'object'])
    gb = df.groupby(group)
    return [data(filename, gb.get_group(x)) for filename, x in  zip(gb.groups.keys(), gb.groups)]


def create_tf_example(group, path):
    with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:
        encoded_jpg = fid.read()
    encoded_jpg_io = io.BytesIO(encoded_jpg)
    image = Image.open(encoded_jpg_io)
    width, height = image.size

    filename = group.filename.encode('utf8')
    image_format = 'jpg'
    xmins = []
    xmaxs = []
    ymins = []
    ymaxs = []
    classes_text = []
    classes = []

    for index, row in group.object.iterrows():
        xmins.append(row['xmin'] / width)
        xmaxs.append(row['xmax'] / width)
        ymins.append(row['ymin'] / height)
        ymaxs.append(row['ymax'] / height)
        classes_text.append(row['class'].encode('utf8'))
        classes.append(class_text_to_int(row['class']))

    tf_example = tf.train.Example(features=tf.train.Features(feature={
        'image/height': dataset_util.int64_feature(height),
        'image/width': dataset_util.int64_feature(width),
        'image/filename': dataset_util.bytes_feature(filename),
        'image/source_id': dataset_util.bytes_feature(filename),
        'image/encoded': dataset_util.bytes_feature(encoded_jpg),
        'image/format': dataset_util.bytes_feature(image_format),
        'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
        'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
        'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
        'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
        'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
        'image/object/class/label': dataset_util.int64_list_feature(classes),
    }))
    return tf_example


def main(_):
    writer = tf.python_io.TFRecordWriter(FLAGS.output_path)
    path = os.path.join(FLAGS.image_dir)
    examples = pd.read_csv(FLAGS.csv_input)
    grouped = split(examples, 'filename')
    for group in grouped:
        tf_example = create_tf_example(group, path)
        writer.write(tf_example.SerializeToString())

    writer.close()
    output_path = os.path.join(os.getcwd(), FLAGS.output_path)
    print('Successfully created the TFRecords: {}'.format(output_path))


if __name__ == '__main__':
    tf.app.run()

假设对于那些运行相同代码的人，我没有发现任何问题

运行的tensorflow和CSV输入无法识别

0 个答案: